Run
10455431

Run 10455431

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.ensemble._hist_gradie nt_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.naive_bayes.BernoulliNB(11)_alpha77.18668680398834
sklearn.naive_bayes.BernoulliNB(11)_binarize0.0
sklearn.naive_bayes.BernoulliNB(11)_class_priornull
sklearn.naive_bayes.BernoulliNB(11)_fit_priortrue
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_l2_regularization4.08110070385719e-06
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_learning_rate0.013807466115157048
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_loss"auto"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_bins20
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_depth4
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_iter731
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_max_leaf_nodes2162
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_min_samples_leaf1037
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_n_iter_no_change92
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_random_state42
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_scoring"f1_micro"
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_tol0.1153683046776128
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_validation_fraction0.2944412972598692
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_verbose0
sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier(7)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier)(1)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

16 Evaluation measures

Area under ROC curve
0.8145 ± 0.0483
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0366 ± 0.0342
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.098 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9461 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9588 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2166 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9589 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)